引用本文:周 洁,范熙伟※,刘耀辉.无人机遥感在塑料大棚识别中的方法研究[J].中国农业信息,2019,31(1):95-111
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无人机遥感在塑料大棚识别中的方法研究
周 洁1, 范熙伟※2, 刘耀辉3,4
1.云南师范大学旅游与地理科学学院,昆明 650500;2.中国地震局地质研究所,北京 100029;3.中国地震局地质研究所,北京 100029,中国;4.昆士兰大学地球与环境科学系,布里斯班 4067,澳大利亚
摘要:
【目的】快速、准确地获得塑料大棚覆盖面积、位置分布等地理信息,对政府统筹规 划、农业产值估算、环境资源保护等方面提供技术支持。【方法】利用无人机高分辨率遥感 数据,以云南省昆明市呈贡区可乐村塑料大棚区为研究区域,分别采用基于像元和面向对 象的方法,对研究区进行分割、分类、面积提取,结合研究区背景资料和实地调研结果, 比较两种方法的精度,并计算了该研究区塑料大棚的面积。【结果】塑料大棚面积的面向 对象提取总体精度为94.6%,Kappa 系数为0.9133; 而基于像元监督分类方法提取总体精 度为68.3%,Kappa 系数为0.5848。面向对象方法提取的面积与实地测量提取面积相比仍 然存在一定的误差,在总面积上的差值为3.925 m2,累积差值为22.475 m2。若按差值比例 来计算,大棚的估计面积为(26.371 42 万±410.737)m2,满足精度要求。【结论】面向对 象方法精度远远高于基于像元方法的精度,且能有效抑制“椒盐现象”,极大降低“同物 异谱”、“同谱异物”的影响,提高了分类的精度。该文研究成果为无人机遥感在塑料大棚 识别应用提供了参考。
关键词:  无人机;面向对象;大棚;遥感;面积提取
DOI:10.12105/j.issn.1672-0423.20190109
分类号:
基金项目:国家重点研发计划(2018YFC1504503)和(2018YFC1504403),“大中城市地震灾害情景构建”重点专项 (2016QJGJ14)和地震行业科研专项结余资金项目“高精度震后快速评估技术研究”的共同支持
Research on the method of UAV remotesensing in plastic greenhouse
Zhou Jie,Fan Xiwei※,Liu Yaohui
1.Yunnan Normal University,School of Tourism and Geographic Science,Kunming 650500,China;2.Institute of Geology,China Earthquake Administration,Beijing 100029,China;3.The University of Queensland,School of Earth and Environmental Sciences,Brisbane 4067,Australia
Abstract:
[Purpose]It is of great significance for the government to make overall plans,estimate the agricultural output value,and protect the environmental resources,by quickly and accurately obtaining the geographical information of the covering area and location distribution of plastic greenhouse.[ Method]With the rapid development of UAV remote sensing,the details contained in remote sensing images are increasingly rich. In this paper,we used UAV high-resolution remote sensing data,which was taken in the plastic greenhouse zone of Kele Village,Chenggong District of Kunming,Yunnan Province,to split,classify,and extract the plastic greenhouse area. The pixel-oriented method and object-oriented method were used in our study. Finally we compared the accuracy of the two methods and calculated the area of the plastic greenhouse in the study area.[ Result]The results show that the overall accuracy of object extraction is 94.6% with a Kappa value of 0.9133,the overall accuracy of pixel extraction is about 68.3% with a Kappa value of 0.5848. The area extracted by the object-oriented method still has some error comparing to the measured area in the field. The difference in the total area is 3.925 m2,and the cumulative difference is 22.475 m2. The estimated area of the greenhouse is 263 714.2±410.737 m2 according to the difference ratio,which meets the accuracy requirements.[ Conclusion]The accuracy of object-oriented method is much higher than that of the pixel-oriented method. Object-oriented method can effectively reduce the“ salt and pepper phenomenon”,which greatly reduces the“ synonyms spectrum” and“ foreign body in the same spectrum”,and finally improves the classification accuracy. Our results could provide a guidance for the methods of UAV remote sensing identification in plastic greenhouse.
Key words:  UAV;object-oriented method;plastic greenhouse;remote sensing;area extraction